用强化学习解耦分析遗传算法参数，发现结构调整比概率微调更重要
Decoupling Numerical and Structural Parameters: An Empirical Study on Adaptive Genetic Algorithms via Deep Reinforcement Learning for the Large-Scale TSP
- 设计双层强化学习框架，分离控制数值与结构参数
- 在rl5915实例上降低45%最优差距，验证策略有效性
- 揭示结构动态调整是突破局部最优的关键，适合算法自动化研究者
恰当的参数配置是进化算法成功的关键。尽管已有多种自适应策略，但各控制维度是否同等影响算法可扩展性仍不清楚。本文将控制变量分为数值参数（如交叉、变异率）和结构参数（如种群大小、算子切换），假设二者作用不同。提出基于双层深度强化学习的实证研究框架，采用循环PPO智能体动态调节参数，将DRL模型作为探测演化动态的工具。实验表明：所学策略优于静态基线，在最大测试实例rl5915上优化差距减少约45%。进一步消融分析揭示：数值调优仅实现局部精炼，而结构灵活性才是防止停滞、跳出局部最优的关键。该结果表明，未来自动算法设计应更注重动态结构重组而非精细概率调整。代码已公开于https://github.com/StarDream1314/DRLGA-TSP。
原文摘要 · Abstract (English)
Proper parameter configuration is a prerequisite for the success of Evolutionary Algorithms (EAs). While various adaptive strategies have been proposed, it remains an open question whether all control dimensions contribute equally to algorithmic scalability. To investigate this, we categorize control variables into numerical parameters (e.g., crossover and mutation rates) and structural parameters (e.g., population size and operator switching), hypothesizing that they play distinct roles. This paper presents an empirical study utilizing a dual-level Deep Reinforcement Learning (DRL) framework to decouple and analyze the impact of these two dimensions on the Traveling Salesman Problem (TSP). We employ a Recurrent PPO agent to dynamically regulate these parameters, treating the DRL model as a probe to reveal evolutionary dynamics. Experimental results confirm the effectiveness of this approach: the learned policies outperform static baselines, reducing the optimality gap by approximately 45% on the largest tested instance (rl5915). Building on this validated framework, our ablation analysis reveals a fundamental insight: while numerical tuning offers local refinement, structural plasticity is the decisive factor in preventing stagnation and facilitating escape from local optima. These findings suggest that future automated algorithm design should prioritize dynamic structural reconfiguration over fine-grained probability adjustment. To facilitate reproducibility, the source code is available at https://github.com/StarDream1314/DRLGA-TSP
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